System and method for authentication enabling bot

ABSTRACT

The present disclosure relates to a system and method for enabling authentication on the bot application. The system may receive video stream along with query from a user computing device associated with a user. The query may be a generic or a privileged query and based on the nature of query, identification and verification of the user is carried out. For privileged services, the authentication procedure may include many levels of authentication processes that may include biometric authentication modules as well. Once the user is verified and authenticated, the response for the user query is provided to the user.

FIELD OF INVENTION

The embodiments of the present disclosure generally relate tofacilitating authentication of users for generation of response to auser query. More particularly, the present disclosure relates to asystem and method for facilitating biometric authentication of a userquery for a category of service for a user based on a machine learningbased architecture where authentication could be upgraded or downgradedbased on the the user query and user equipment.

BACKGROUND OF THE INVENTION

The following description of related art is intended to providebackground information pertaining to the field of the disclosure. Thissection may include certain aspects of the art that may be related tovarious features of the present disclosure. However, it should beappreciated that this section be used only to enhance the understandingof the reader with respect to the present disclosure, and not asadmissions of prior art.

Authentication systems are the protective barrier for most systems. Itmakes sure that right people enter the system and access the rightinformation. For example, User A only has access to relevant informationand cannot see the sensitive information of User B.

Passwords are the most common methods of authentication. Passwords canbe in the form of a string of letters, numbers, or special characters.However, passwords are prone to phishing attacks and bad hygiene thatweakens effectiveness. An average person has about 25 different onlineaccounts, but only 54% of users use different passwords across theiraccounts. The truth is that there are a lot of passwords to remember. Asa result, many people choose convenience over security. Most people usesimple passwords instead of creating reliable passwords because they areeasier to remember. The bottom line is that passwords have a lot ofweaknesses and are not sufficient in protecting online information.Hackers can easily guess user credentials by running through allpossible combinations until they find a match. Whereas, Multi-FactorAuthentication (MFA) is an authentication method that requires two ormore independent ways to identify a user. Examples include codesgenerated from the user's smartphone, Captcha tests, fingerprints, orfacial recognition. MFA authentication methods and technologies increasethe confidence of users by adding multiple layers of security. MFA maybe a good defense against most account hacks, but it has its ownpitfalls. People may lose their phones or SIM cards and not be able togenerate an authentication code.

Further, certificate-based authentication technologies identify users,machines or devices by using digital certificates. The certificatecontains the digital identity of a user including a public key, and thedigital signature of a certification authority. Users provide theirdigital certificates when they sign in to a server. The server verifiesthe credibility of the digital signature and the certificate authority.The server then uses cryptography to confirm that the user has a correctprivate key associated with the certificate. Token-based authenticationtechnologies enable users to enter their credentials once and receive aunique encrypted string of random characters in exchange.

While, biometrics authentication is a security process that relies onthe unique biological characteristics of an individual. Biologicalcharacteristics can be easily compared to authorized features saved in adatabase. Common biometric authentication methods include: Facialrecognition matches the different face characteristics of an individualtrying to gain access to an approved face stored in a database. Facerecognition can be inconsistent when comparing faces at different anglesor comparing people who look similar, like close relatives. Fingerprintscanners match the unique patterns on an individual's fingerprints. Somenew versions of fingerprint scanners can even assess the vascularpatterns in people's fingers. Voice identification examines a speaker'sspeech patterns for the formation of specific shapes and soundqualities. A voice-protected device usually relies on standardized wordsto identify users, just like a password. Eye scanners includetechnologies like iris recognition and retina scanners. Iris scannersproject a bright light towards the eye and search for unique patterns inthe colored ring around the pupil of the eye. The patterns are thencompared to approved information stored in a database. Eye-basedauthentication may suffer inaccuracies if a person wears glasses orcontact lenses. Biometric authentication traditionally are mostly usedto control physical access when installed on gates and doors.

In the last few decades, entities/organizations have been marketingtheir products by online means wherein there exists a facility for theusers/customers to do an online textual chat with a bot for getting textbased responses for various queries that any user may have aboutproducts/operational services provided by such entities. But, suchonline means can be easily hacked by the existing authenticationtechnologies. Even a video based response such as a video recording thatmay pictorially demonstrate the relevant information related to the userquery can be easily hacked. Even though, many diverse forms ofauthentication processes are present, much to the chagrin of thesecurity community, passwords have stubbornly remained the onlyauthentication mechanism in place for the vast majority of useraccounts. This is largely due to the simplicity and ease of use thatpasswords provide account holders.

Also, existing authentication techniques cannot be customised. Moreover,existing personalised bot interaction cannot provide sensitivepersonalized information like financial and medical records withadditional features of accuracy, quick response and fraud-freeoperations while sharing or receiving any sensitive information, thetechnology offers a staggered level of security for compliance beforeany transaction can be initiated. Even if the existing bots are able toprovide some amount of personalised sensitive information, users oftenneed to travel to respective centres to undertake transactionsparticularly in cases where personal presence is required to preventfrauds. The existing bots are also not customer friendly, easy tointeract with and further do not provide accurate and timely interactionwith the customer while securing and authenticating the users. Forexample, customers may want to know their banking transaction details,credit card details, profile related details, and the without any dangerof their sensitive information being compromised and hence always mayprefer to visit their respective banks. Without a good authenticationtechnique, customers may not want to get or send their sensitivepersonalized information as well as get any queries/requirementsresolved on existing bot interaction itself without visiting the branchor calling the customer care.

There is, therefore, a need in the art to provide a system and a methodthat can allow an interactive bot as effective tool not only to answergeneric queries by displaying graphics, images, textual messages, audiosand videos on the bot through which the user could get resolution tohis/her requirements but also for customized queries to configurevarious call-to-actions on the bot with enhanced authentication featuresto provide accuracy and security to enable sharing personalized andcustomized information to the users.

OBJECTS OF THE PRESENT DISCLOSURE

Some of the objects of the present disclosure, which at least oneembodiment herein satisfies are as listed herein below.

It is an object of the present disclosure to provide a system and amethod for facilitating enhanced authentication features to provideaccuracy and security to enable sharing personalized and customizedinformation to the users.

It is an object of the present disclosure to provide a system and amethod for reducing or eliminating the need for users to physicallyvisit an entity in order to complete a transaction.

It is an object of the present disclosure to provide a system and amethod for facilitating secure exchange of sensitive information.

It is an object of the present disclosure to provide a system and amethod for ensuring fraud free operations.

It is an object of the present disclosure to provide an approach forvalidating identity of the user and ensure cost effective services toaddress user needs by a simple authenticating but proactiveinfrastructure.

It is an object of the present disclosure to provide a system and amethod that facilitates an interactive bot as effective tool to answergeneric queries by displaying graphics, images, textual messages, audiosand videos on the bot through which the user could get resolution tohis/her requirements.

It is an object of the present disclosure to provide a system and methodfor customized queries to configure various call-to-actions on the bot.

It is an object of the present disclosure to provide with enhancedauthentication features to provide accuracy and security to enablesharing personalized and sensitive information to the users.

SUMMARY

This section is provided to introduce certain objects and aspects of thepresent invention in a simplified form that are further described belowin the detailed description. This summary is not intended to identifythe key features or the scope of the claimed subject matter.

In order to achieve the aforementioned objectives, the present inventionprovides a system and method for facilitating authentication on the botapplication. In an aspect, the system for authentication on the botapplication may include a processor that executes a set of executableinstructions stored in a memory, upon execution of which, the processorcauses the system to receive a first set of data packets that mayinclude a video stream along with query from a user computing deviceassociated with a user. The video stream along with the query maypertain to biometric features of the user, and receive, from a database,a knowledgebase that may include a set of potential identity informationassociated with the biometric features of the user and a plurality ofinformation services associated with the user and said query. The systemalso causes the processor to extract a first set of features and asecond set of features from the received first set of data packets. Thefirst set of features may be associated with a class of queriesassociated with the information service and may be extracted by a queryextraction engine and a bio-metric collecting engine may extract thesecond set of features, where the second set of features correspond tobiometric features of said user. The system further causes the processorto map, through a machine learning (ML) engine, any or a combination ofextracted first and second set of features with said knowledgebase toidentify and authenticate the user and the query. Furthermore, thesystem may cause the processor to generate, through the ML engine, atrained model configured to process the query of the identified andauthenticated user, and predict, from the plurality of informationservices, an information service associated with the identified andauthenticated user query, and facilitate response corresponding to theinformation service to the identified and authenticated user query basedon the trained model and then the system may cause the processor toauto-generate, using the ML engine, the response by the bot applicationto the identified and authenticated user.

In another aspect, the present disclosure includes method for system forauthentication on the bot application. The method may be executed by aprocessor, and includes the steps of: receiving a first set of datapackets comprising a video stream along with query from a user computingdevice associated with a user, the video stream along with the querypertaining to biometric features of the user, and receiving, from adatabase, a knowledgebase that may include a set of potential identityinformation associated with the biometric features of the user and aplurality of information services associated with the user and thequery. Further, the method may include the step of extracting, by aquery extraction engine, a first set of features from the received firstset of data packets, the first set of features associated with a classof queries associated with the information service and extracting, by abiometric collecting engine, a second set of features from the receivedfirst set of data packets, the second set of features corresponding tobiometric features of said user. Furthermore, the method may include thestep of mapping, through a machine learning (ML) engine, any or acombination of extracted first and second set of features with saidknowledgebase to identify and authenticate the user and the query andgenerating, through the ML engine, a trained model configured to processsaid query of said identified and authenticated user, and predict, fromsaid plurality of information services, an information serviceassociated with the identified and authenticated user query, andfacilitate response corresponding to the information service to theidentified and authenticated user query based on the trained model. Themethod may also include the step of auto-generating, using the MLengine, the response by the bot application to the identified andauthenticated user.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings, which are incorporated herein, and constitutea part of this invention, illustrate exemplary embodiments of thedisclosed methods and systems in which like reference numerals refer tothe same parts throughout the different drawings. Components in thedrawings are not necessarily to scale, emphasis instead being placedupon clearly illustrating the principles of the present invention. Somedrawings may indicate the components using block diagrams and may notrepresent the internal circuitry of each component. It will beappreciated by those skilled in the art that invention of such drawingsincludes the invention of electrical components, electronic componentsor circuitry commonly used to implement such components.

FIG. 1 illustrates an exemplary network architecture in which or withwhich the system of the present disclosure can be implemented, inaccordance with an embodiment of the present disclosure.

FIG. 2 illustrates an exemplary representation (200) of system (110) ora centralized server (112), in accordance with an embodiment of thepresent disclosure.

FIG. 3 illustrates exemplary method flow diagram (300) depicting amethod for facilitating authorization on the bot application, inaccordance with an embodiment of the present disclosure.

FIG. 4 illustrates an exemplary representation (300) of systemarchitecture and its implementation, in accordance with an embodiment ofthe present disclosure.

FIGS. 5A and 5B illustrate exemplary flow diagrams representingregistration and verification process of a user, in accordance with anembodiment of the present disclosure.

FIGS. 6A-6J illustrate exemplary interfaces of the bot, in accordancewith an embodiment of the present disclosure.

FIG. 7 refers to an exemplary flow diagram representing user interactionwith the bot, in accordance with an embodiment of the presentdisclosure.

The foregoing shall be more apparent from the following more detaileddescription of the invention.

BRIEF DESCRIPTION OF INVENTION

In the following description, for the purposes of explanation, variousspecific details are set forth in order to provide a thoroughunderstanding of embodiments of the present disclosure. It will beapparent, however, that embodiments of the present disclosure may bepracticed without these specific details. Several features describedhereafter can each be used independently of one another or with anycombination of other features. An individual feature may not address allof the problems discussed above or might address only some of theproblems discussed above. Some of the problems discussed above might notbe fully addressed by any of the features described herein.

The present invention provides a robust and effective solution to anentity or an organization by enabling them to implement a system forfacilitating authentication on a bot as well as ability to customizeresponses to any queries that may be asked by users using their devices,wherein the queries may be related to one or more aspects ofoperational, information services/goods of the entity. The presentinvention provides for the bot that may allow remote authentication ofuser for services provided by the entity or organisation using this bot.The present invention thus can enable the user to provide and receivesensitive personal and may eliminate the need to travel by the user toundertake transactions particularly in cases where personal presence isrequired. The present invention may further enable accurate and timelyinteraction with the user and ensure fraud free operations.

Referring to FIG. 1 that illustrates an exemplary network architecture(100) in which or with which system (110) of the present disclosure canbe implemented, in accordance with an embodiment of the presentdisclosure. As illustrated in FIG. 1, by way of example and notlimitation, the exemplary architecture (100) may include a plurality ofusers (102-1, 102-2, . . . 102-n) (hereinafter interchangeably referredas user 102 and collectively referred to as users 102). Each user may beassociated with at least one user computing device (120-1, 120-2, . . .120-n) (hereinafter interchangeably referred as a user device 120 andcollectively referred to as user devices 120 or user equipments 120).The users (102) may interact with the system (110) by using theirrespective user device (120). The user device (120) and the system (110)may communicate with each other over a network (106). The system (110)may be associated with a centralized server (112). Examples of thecomputing devices (120) can include, but are not limited to a smartphone, a portable computer, a personal digital assistant, a handheldphone, a laptop, a smart TV, a set top box (STB), a mobile phone and thelike.

More specifically, the exemplary architecture (100) includes a system(110) equipped with a machine learning (ML) engine (216) forfacilitating authentication of the user (102) on the bot that canreceive a first set of data packets that may include a video streamalong with query from the user computing device (120). The video streamalong with the query may pertain to biometric features of the user(102). The system (110) may include a database (210) that may store aknowledgebase having a set of potential identity information associatedwith the biometric features of the user (102) and a plurality ofinformation services associated with the user (102) and the querygenerated by the user. The user device (120) may be communicably coupledto the centralized server (110) through the network (106) to facilitatecommunication therewith. As an example and not by way of limitation,network architecture (100) may include a second computing device (104)(also referred to as computing device hereinafter) associated with anentity (114). The computing device (104) may be operatively coupled tothe centralised server (112) through the network (106).

In an embodiment, the system (110)/centralised server (112) may includefeature extraction engine (214). The feature extraction engine (214) maybe operatively coupled to a query extraction engine and a biometriccollection engine. The query extraction engine may be configured toextract a first set of features from the received first set of datapackets, where the first set of features may be associated with a classof queries pertaining to an information service, while the biometriccollection engine may be configured to extract a second set of featuresfrom the received first set of data packets, where the second set offeatures may correspond to biometric features of the user. In anembodiment, the ML engine (216) may be then configured to map any or acombination of extracted first and second set of features with saidknowledgebase to identify and authenticate the user and the query.

In an embodiment, the system (110)/server (112) may further configurethe ML engine (216) to generate, through an appropriately selectedmachine learning (ML) model of the system in a way of example and not aslimitation, a trained model configured to process the query of theidentified and authenticated user, and predict, from the plurality ofinformation services, an information service associated with theidentified and authenticated user query, and facilitate responsecorresponding to the information service to the identified andauthenticated user query based on the trained model. Generate, throughthe ML engine, a trained model configured to process said query of saididentified and authenticated user, and predict, from said plurality ofinformation services, an information service associated with saididentified and authenticated user query, and facilitate responsecorresponding to said information service to said identified andauthenticated user query based on the trained model. The ML engine (216)may be further configured to auto-generate the response by the bot tothe identified and authenticated user.

In an embodiment, the class of queries may include a set of queries forinformation services that may include generic information services andprivileged information services.

In another embodiment, as a way of example and not as a limitation, thequery of the identified and authenticated user may be received at clientside of the executable bot in the form of a second set of data packetsfrom said user device (120). In another embodiment, the response mappedwith the information service may be transmitted in real-time in the formof a third set of data packets to said user device (120) from serverside of the executable bot.

In an exemplary embodiment, the executable bot may be represented in theform of any or a combination of an animated character, a personalitycharacter, an actual representation of a human operator and the like.

In an embodiment, the system may be configured to obtain a registrationdata based on a request from an unregistered user through respectiveuser device (120). In an exemplary embodiment, the login credentials maybe generated based on acknowledgement of the request and verification ofthe registration data. In another exemplary embodiment, the user (102)may enter the generated login credentials to access the system to obtainthe information service associated with the user (102).

In yet another embodiment, the system (110) may store consent of theuser to store biometric features of the user (102) for the class ofqueries for information services that may include privileged informationservices and upon receipt of the consent of the user the system (110)may store the biometric features of the user. In another embodiment, thebiometric features may be stored based on the biometric scannersavailable in the user computing device (120) associated with the user(102).

In another embodiment, the ML engine (216) may identify and authenticatethe user (102) through any or a combination of voice, password, OTP,facial feature, fingerprint, iris, DNA, skin, ear lobe, nose but notlimited to these stored in the database. In yet another embodiment theML engine (216) may be configured to identify whether the query is forgeneric information services or privileged information services.Further, in another embodiment, the ML engine may check whether theconsent of user is available to access the privileged informationservices. In yet another embodiment, the ML engine (216) may beconfigured to apply and identify one or more authentication modulesbased on a predefined set of configuration parameters associated withthe plurality of information services corresponding to the querygenerated by the user (102).

In an exemplary embodiment, the predefined configuration parameters maypertain to the availability of biometric feature extracting and scanningdevices coupled to the user device (120) and the class of queriescorresponding to generic information services and privileged informationservices.

In an aspect, the bot application may be generated through any or acombination of IVR, Native Dialler, OTT route and the like.

The ML engine (216) in another embodiment, may be configured to changeauthentication module based on any or a combination of the querygenerated by the user (102) and user equipment associated with therespective biometric features. The change in authentication module maycorrespond to upgradation or down gradation of the authentication modulehaving the biometric features. In an exemplary embodiment, by way ofexample and not as limitation, if a user may want only generic servicesfrom a service provider, such as a query for banking services to knowaccount balance, authentication module may provide only basicauthentication process such as asking for passwords, OTPs and the like.In another exemplary embodiment, if the user wants a privileged service,the authentication module may be upgraded to add authentication featuressuch as biometric scan including Iris scan, fingerprint scan, ear lobescan, facial feature scan and the like. In another embodiment, theauthentication module may be downgraded if the user equipment is unableto support biometric scanners for scanning Iris, ear lobe, fingerprint,facial features and the like.

In yet another embodiment, the ML engine may be configured to receive aquery in the form of any or a combination of textual message, audioform, video form but not limited to it and the response associated withthe information service corresponding to the query received by the MLengine may also be provided in the form of textual message, audio form,video form but not limited to it.

Further, in an embodiment, the ML engine may be configured with languageprocessing engines to receive the query in any language and provide theresponse corresponding to the query in any language.

In a way of example, but not as a limitation, the query can be in anylanguage such as Hindi, English, Assamese, Bengali, Kannada, French,Korean and the like and the response for the query can be in anylanguage such as Hindi, English, Assamese, Bengali, Kannada, French,Korean and the like.

In an embodiment, the pre-defined responses may be generated by theentity (114) using the computing device (104) based on one or morerequirement criteria. The ML engine (216) may be provided an inputincluding the pre-defined queries and the correspondingresponses/datasets to enable a learning phase of the ML engine (216).The user (102) may ask a query using his/her user device (120),and basedon the user query and intent/category/classification that the query maybe processed/mapped to, the system (110) may generate one or moreresponses. The responses may be provided as video streams.

The system (110) of the present disclosure can enable entity (114) tocustomize the pre-defined responses in a manner that may best suit theneeds of the entity (114) for enhanced awareness of the informationalservices offered by them. In an embodiment, the pre-defined responses asvideo streams (input) and the automated responses (output) may includeany or a combination of responsive video frames and visual display ofinformation including, but not limited to, graphical data and imagesthat may be informative with respect to the pre-defined query. In anexemplary embodiment, the responsive video frames may be video recordingthat may be manually recorded using a recording device coupled to thecomputing device (104) of the entity (114). The recording device can beany or a combination of a camera, a video recorder and the like that maybe either inbuilt or externally connected to the computing device (104)of the entity (102). The recording device may further include one ormore audio recording accessories connected thereto. In an embodiment,the manual recording may be done based on an authentication of anidentity of the entity or one or more operators associated with theentity (102), such that only if the authentication may be positive, theentity or the operator may be allowed to manually record the responsivevideo frames. Based on positive authentication, the computing device(104) may be communicably coupled via an interface of the system (110)such that bot engine of the system (110) may receive the pre-definedvisual responses through an interface of the system (110).

In an embodiment, the requirement criteria for generation of thepre-defined visual/video frame responses can include at least one factorassociated with the pre-defined query selected from relevancy ofinformation, theoretical information, information related to theavailability of one or more products corresponding to the operationalservices and a recommendation corresponding to the operational services.In an exemplary embodiment, the entity (114) may desire to generatepre-defined response based on the relevancy of information, wherein therelevancy can depend on the qualitative information that may beessential to explain a particular pre-defined query. The theoreticalinformation may be related to the existing general information inrelation to the pre-defined query. The information related to theavailability of one or more products correspond to the operationalservices that may include data which may be specific to the type andvariety of products that the entity might be offering. Therecommendation corresponding to the operational services may include anopinion or a perspective that may highlight which products may be moresuited for a specific set of users. Thus, the present system can enablea wide variety of responses and hence can be far more effective as wellas informative.

In accordance with an embodiment and as illustrated in FIG. 1, on theuser end, the architecture can enable an user to access informationregarding the information services offered by the entity (114) by typinga user query (hereinafter interchangeably referred to as query/queries)on their respective user devices (120) and obtaining a visual responsefor the user query. In an embodiment, the user can gain access to thesystem only when he/she has been identified and authorized by thesystem. In an embodiment, the user may include, but not limited to, anexisting customer, a potential customer, a research analyst, or anyother person interested to know about the services offered by theentity.

In an embodiment, the computing device (104) and/or the user device(120) may communicate with the system (110) via set of executableinstructions residing on any operating system, including but not limitedto, Android™, iOS™, Kai OS™ and the like. In an embodiment, computingdevice (104) and/or the user device (120) may include, but not limitedto, any electrical, electronic, electro-mechanical or an equipment or acombination of one or more of the above devices such as mobile phone,smartphone, virtual reality (VR) devices, augmented reality (AR)devices, laptop, a general-purpose computer, desktop, personal digitalassistant, tablet computer, mainframe computer, a smart TV, a Set TopBox (STB) or any other computing device, wherein the computing devicemay include one or more in-built or externally coupled accessoriesincluding, but not limited to, a visual aid device such as camera, audioaid, a microphone, a keyboard, input devices for receiving input from auser such as touch pad, touch enabled screen, electronic pen and thelike. It may be appreciated that the computing device (104) and/or theuser device (120) may not be restricted to the mentioned devices andvarious other devices may be used. A smart computing device may be oneof the appropriate systems for storing data and other private/sensitiveinformation.

In an exemplary embodiment, a network 106 may include, by way of examplebut not limitation, at least a portion of one or more networks havingone or more nodes that transmit, receive, forward, generate, buffer,store, route, switch, process, or a combination thereof, etc. one ormore messages, packets, signals, waves, voltage or current levels, somecombination thereof, or so forth. A network may include, by way ofexample but not limitation, one or more of: a wireless network, a wirednetwork, an internet, an intranet, a public network, a private network,a packet-switched network, a circuit-switched network, an ad hocnetwork, an infrastructure network, a public-switched telephone network(PSTN), a cable network, a cellular network, a satellite network, afiber optic network, some combination thereof.

In another exemplary embodiment, the centralized server 110 may includeor comprise, by way of example but not limitation, one or more of: astand-alone server, a server blade, a server rack, a bank of servers, aserver farm, hardware supporting a part of a cloud service or system, ahome server, hardware running a virtualized server, one or moreprocessors executing code to function as a server, one or more machinesperforming server-side functionality as described herein, at least aportion of any of the above, some combination thereof.

In an embodiment, the system (110) may include one or more processorscoupled with a memory, wherein the memory may store instructions whichwhen executed by the one or more processors may cause the system toperform the generation of automated visual responses to a query. FIG. 2with reference to FIG. 1, illustrates an exemplary representation ofsystem (110)/centralized server (112) for facilitating authorization onthe bot through which one or more automated visual responses to a userquery are transmitted based on a machine learning based architecture, inaccordance with an embodiment of the present disclosure. In an aspect,the system (110)/centralized server (112) may comprise one or moreprocessor(s) (202). The one or more processor(s) (202) may beimplemented as one or more microprocessors, microcomputers,microcontrollers, digital signal processors, central processing units,logic circuitries, and/or any devices that process data based onoperational instructions. Among other capabilities, the one or moreprocessor(s) (202) may be configured to fetch and executecomputer-readable instructions stored in a memory (206) of the system(110). The memory (206) may be configured to store one or morecomputer-readable instructions or routines in a non-transitory computerreadable storage medium, which may be fetched and executed to create orshare data packets over a network service. The memory (206) may compriseany non-transitory storage device including, for example, volatilememory such as RAM, or non-volatile memory such as EPROM, flash memory,and the like.

In an embodiment, the system (110)/centralized server (112) may includean interface(s) 204. The interface(s) 204 may comprise a variety ofinterfaces, for example, interfaces for data input and output devices,referred to as I/O devices, storage devices, and the like. Theinterface(s) 204 may facilitate communication of the system (110). Theinterface(s) 204 may also provide a communication pathway for one ormore components of the system (110) or the centralized server (112).Examples of such components include, but are not limited to, processingengine(s) 208 and a database 210.

The processing engine(s) (208) may be implemented as a combination ofhardware and programming (for example, programmable instructions) toimplement one or more functionalities of the processing engine(s) (208).In examples described herein, such combinations of hardware andprogramming may be implemented in several different ways. For example,the programming for the processing engine(s) (208) may be processorexecutable instructions stored on a non-transitory machine-readablestorage medium and the hardware for the processing engine(s) (208) maycomprise a processing resource (for example, one or more processors), toexecute such instructions. In the present examples, the machine-readablestorage medium may store instructions that, when executed by theprocessing resource, implement the processing engine(s) (208). In suchexamples, the system (110)/centralized server (112) may comprise themachine-readable storage medium storing the instructions and theprocessing resource to execute the instructions, or the machine-readablestorage medium may be separate but accessible to the system (110)/centralized server (112) and the processing resource. In otherexamples, the processing engine(s) (208) may be implemented byelectronic circuitry.

The processing engine (208) may include one or more engines selectedfrom any of a data acquisition (212), a feature extraction (214), amachine learning (ML) engine (216), and other engines (218). In anembodiment, the data acquisition engine (212) of the system (110) canreceive/process/pre-process comprising a video stream along with queryfrom a user computing device (120) associated with a user (102), thevideo stream along with the query may include biometric features of theuser and a knowledgebase (retrieved say from a database or a storagemedium (210) including, but not limited to, one or more potentialqueries that the entity is likely to be asked along with video frameresponses to each of the one or more potential queries. Each query maybe associated/mapped with an intent/category/classification that mayreflect the purpose/intent behind the query. The bot engine also enablesgeneration of plurality of datasets based on one or more pre-definedvisual/video frame responses and pre-defined/potential queries receivedfrom the computing device (104) of the entity (114). The dataacquisition engine (212) can receive pre-defined visual responses fromthe computing device (104) through an interface of the system and storethem in a database (210) based on prestored parameters associated witheach pre-defined query.

In an embodiment, the proposed system may include a feature extractionengine (214) configured to extract features associated with thefacilitation of authentication of users through the bot. The featureextraction engine (214) may include a query extraction engine that mayextract a first set of features pertaining to query generated by theuser from the receive first set of data packets. In an exemplaryembodiment, the feature extraction engine may also include a biometricfeature collection engine that may be configured to extract a second setof features associated with the biometric features of the user. As a wayof example, and not as a limitation, the biometric features may includeany feature related to facial image, iris, DNA, fingerprint, voice, earlobe, nose and the like.

In an aspect, the ML engine (216) can be configured to generate, througha machine learning (ML) model of the system, a trained model configuredto process the query of the identified and authenticated user, andpredict, from the plurality of information services, an informationservice associated with the identified and authenticated user query, andfacilitate response corresponding to the information service to theidentified and authenticated user query based on the trained model.generate, through the ML engine, a trained model configured to processsaid query of said identified and authenticated user, and predict, fromsaid plurality of information services, an information serviceassociated with said identified and authenticated user query, andfacilitate response corresponding to said information service to saididentified and authenticated user query based on the trained model. TheML engine (216) may be further configured to auto-generate the responseby the bot to the identified and authenticated user.

In an aspect, the end-user query can be received at client side of theexecutable bot application in the form of a first set of data packetsfrom an end user computing device, and wherein the video frame responsethat is mapped with the predicted intent can be transmitted in real-timein the form of a second set of data packets to said end user computingdevice from server side of the executable bot application. In anotheraspect, the client side of the executable bot application can berepresented in the form of any or a combination of an animatedcharacter, a personality character, or an actual representation of theentity character.

FIG. 3 illustrates exemplary method flow diagram (300) depicting amethod for facilitating authorization on the bot application, inaccordance with an embodiment of the present disclosure.

As illustrated, in an aspect the method may facilitate authorization onthe bot through a series of steps. The method may include at 302, thestep for receiving a first set of data packets comprising a video streamalong with query from a user computing device associated with a user,the video stream along with the query pertaining to biometric featuresof the user, and receiving, from a database, a knowledgebase that mayinclude a set of potential identity information associated with thebiometric features of the user and a plurality of information servicesassociated with the user and the query. Further, the method may includeat 304, the step for extracting, by a query extraction engine, a firstset of features from the received first set of data packets, the firstset of features associated with a class of queries associated with theinformation service and at 306, a step for extracting, by a biometriccollecting engine, a second set of features from the received first setof data packets, the second set of features corresponding to biometricfeatures of said user.

Furthermore, the method may include at 308, the step for mapping,through a machine learning (ML) engine, any or a combination ofextracted first and second set of features with said knowledgebase toidentify and authenticate the user and the query and at 310, the stepfor generating, through the ML engine, a trained model configured toprocess said query of said identified and authenticated user, andpredict, from said plurality of information services, an informationservice associated with the identified and authenticated user query, andfacilitate response corresponding to the information service to theidentified and authenticated user query based on the trained model. Themethod may also include at 312 the step for auto-generating, using theML engine, the response by the bot application to the identified andauthenticated user.

FIG. 4 illustrates an exemplary representation (400) of systemarchitecture and its implementation, in accordance with an embodiment ofthe present disclosure.

As illustrated, the system architecture may include the registrationprocess and the verification process. The registration process mayinclude user registration (402) of the user (102) through biometricfeature identity such as facial identification, finger-print, iris usingnon-public registration portals. The signed consent of user would alsobe taken in the registration process through the user registration andconsent interface (404) and would be updated in the system. Onceregistration is completed, the biometric data would be stored inDistributed Blob storage 408. The user (102) may have ability to removehis consent any time. The user registration and consent interface (404)also may include services such as consent service (410), verificationservice (412) and PI service (414). These services are part of botservice (422).

In an embodiment, the user (102) can be verified by the verificationprocess (416) through scanning biometric features such as facialidentification, finger-print, iris and mapping them with the respectivebiometric features stored in the non-public registration portals. Whileinteracting with the Bot through the interacting module (418), user canquery generic information or a privileged information. The Bot service(422) will decide whether the information is generic or privileged. Incase of privileged information, the Bot service (422) may check whetherthe user has given consent to access privileged information through theconsent service (410). In case the user has consented then the botservice (422) may check if the device has hardware capability andpermission for biometric identification. Depending on the degree ofrestriction on the information one or more type of biometricverification would be performed. Once the Biometric identification issuccessful, the privileged information of the user would be presented inthe Bot.

FIGS. 5A and 5B illustrate exemplary flow diagrams representingregistration and verification process of a user, in accordance with anembodiment of the present disclosure.

As illustrated, FIG. 5A, shows a flow diagram associated with aregistration process of a user. The registration process may include atblock 502, User visits non-public Registration Webpage (for examplebranch of the user for higher security). At block 504, the user providesconsent to Bot based authentication and consent is stored in the backendand at block 506, the biometric identification of the user is capturedand stored. Then at block 508, the biometric information of the user isencrypted and stored in Blob storage.

In FIG. 5B, the verification process is highlighted by the flow diagram.The verification process may include at block 510, the user asking forprivileged information. At block 512, the consent of the user is check.Further, at block 514, the hardware capability of the user is checkedafter receiving the consent from the user. At block 516, the biometricidentification of the user is verified and once verified at block 518privileged information of the user is fetched from the backend and atblock 520, Information is presented to the user.

FIGS. 6A-6J illustrate exemplary interfaces of the bot, in accordancewith an embodiment of the present disclosure.

As illustrated in FIG. 6A, the interface shows about a user query for aprivileged information where bot service identifies the intent andstarts the flow to authenticate user. FIG. 6B shows that the user mustbe registered and consented to access privileged information and thefollowing error is shown when the user is not registered or consented.FIG. 6B also shows that before querying for the information at backend,the device hardware capability and permission is checked.

FIG. 6C shows that before querying for the information at backend thedevice hardware capability and permission is checked while FIG. 6D showsthat once the user is connected to the Video Bot, the relevantprivileges are checked such as the consent given by the user to shareprivileged information and post this process a biometric verificationwould be performed. FIG. 6E shows how the system initiates the correctbiometric verification for sharing the relevant privileged information.It waits for a confirmation from the backend servers to complete thisprocess while FIG. 6F illustrates when verification fails through thebiometric process, the secure information is not given out to theuser/customer and he/she is asked to ask the next question by a tap onthe MIC button or simply writing a text.

In an exemplary implementation, FIG. 6G shows how if the user asks aquery about his/her “bank balance” which is a privileged information,the verification process would be triggered again as shown in thescreenshot and FIG. 6H shows the system initiating the correct biometricverification for sharing the relevant privileged information and waitingfor a confirmation from the backend servers to complete this process.And in FIG. 6I, by way of an example and not as a limitation, if theIris scan and Face ID are successful, the user is directed to viewhis/her privileged information as shown in the screenshot below and FIG.6J the relevant account balance information for the customer isdisplayed.

FIG. 7 refers to an exemplary flow diagram representing user interactionwith the bot, in accordance with an embodiment of the presentdisclosure.

As illustrated, the user interaction with the bot may include at block702 user call through native Dialer/IVR/VOIP/OTT BOT. Then at block 704,the user is authenticated using Face Recognition/Finger-Print/IrisRecognition and other authentication services. Further at block 706,completion of transaction takes place and then at block 708, the call isterminated.

In an embodiment, the bot may be invocated through a smart phone, asmart TV, a tablet, a laptop, a set top box (STB) a regular phone, anycomputing device but not limited to the like.

Thus, the present disclosure provides a unique and inventive solutionfor identification and authorization of users to access a system for aparticular service. The authorization can be for a generic service orfor privileged services. The system can enable enhanced authorizationand verification modules for accessing privileged services. Further thesystem may check for the consent of the user and only then providebiometric identification and authorization of the users.

While considerable emphasis has been placed herein on the preferredembodiments, it will be appreciated that many embodiments can be madeand that many changes can be made in the preferred embodiments withoutdeparting from the principles of the invention. These and other changesin the preferred embodiments of the invention will be apparent to thoseskilled in the art from the disclosure herein, whereby it is to bedistinctly understood that the foregoing descriptive matter to beimplemented merely as illustrative of the invention and not aslimitation.

ADVANTAGES OF THE PRESENT DISCLOSURE

The present disclosure provides for a system and a method forfacilitating enhanced authentication features to provide accuracy andsecurity to enable sharing personalized and customized information tothe users.

The present disclosure provides for a system and a method for reducingor eliminating the need for users to physically visit an entity in orderto complete a transaction.

The present disclosure provides for a system and a method forfacilitating secure exchange of sensitive information.

The present disclosure provides for a system and a method for ensuringfraud free operations.

The present disclosure provides for a system and a method for validatingidentity of the user and ensure cost effective services to address userneeds by a simple authenticating but proactive infrastructure.

The present disclosure provides for a system and a method thatfacilitates an interactive bot as effective tool to answer generic andprivileged queries by displaying graphics, images, textual messages,audios and videos on the bot through which the user could get resolutionto his/her requirements.

The present disclosure provides for a system and a method for customizedqueries to configure various call-to-actions on the bot.

The present disclosure provides for a system and a method with enhancedauthentication features to provide accuracy and security to enablesharing personalized and sensitive information to the users.

The present disclosure provides for a system and a method thatfacilitates quick and accurate matching of the biometric features withthe database resulting in instant user verification.

The present disclosure provides for a system and a method that enablesensuring of any advanced verification is undertaken based on the natureof service requested.

The present disclosure provides for a system and a method thatfacilitates assurance that registration process, permission onprivileges and verification processes are seamlessly integrated toprovide optimal user service.

We Claim:
 1. A system for authentication on the bot application, saidsystem comprising a processor that executes a set of executableinstructions that are stored in a memory, upon which execution, theprocessor causes the system to: receive a first set of data packetscomprising a video stream along with query from a user computing deviceassociated with a user, said video stream along with said querypertaining to biometric features of the user, and receive, from adatabase, a knowledgebase comprising a set of potential identityinformation associated with said biometric features of said user and aplurality of information services associated with said user and saidquery; extract, by a query extraction engine, a first set of featuresfrom the received first set of data packets, said first set of featuresassociated with a class of queries associated with the informationservice; extract, by a bio-metric collecting engine, a second set offeatures from the received first set of data packets, said second set offeatures corresponding to biometric features of said user; map, througha machine learning (ML) engine, any or a combination of extracted firstand second set of features with said knowledgebase to identify andauthenticate said user and said query; generate, through the ML engine,a trained model configured to process said query of said identified andauthenticated user, and predict, from said plurality of informationservices, an information service associated with said identified andauthenticated user query, and facilitate response corresponding to saidinformation service to said identified and authenticated user querybased on the trained model; and auto-generate, using the ML engine, theresponse by the bot application to said identified and authenticateduser.
 2. The system as claimed in claim 1, wherein the class of queriescomprise a set of queries for information services, wherein theinformation services are generic information services and privilegedinformation services.
 3. The system as claimed in claim 1, wherein saidquery of the identified and authenticated user is received at clientside of the executable bot application in the form of a second set ofdata packets from said user computing device, wherein said response thatis mapped with the information service is transmitted in real-time inthe form of a third set of data packets to said user computing devicefrom server side of the bot application.
 4. The system as claimed inclaim 1, wherein said bot application is represented in the form of anyor a combination of an animated character, a personality character, oran actual representation of a human operator.
 5. The system as claimedin claim 1, wherein the system is configured to obtain a registrationdata based on a request from an unregistered user through respectiveuser computing device, wherein login credentials are generated based onacknowledgement of the request and verification of the registrationdata, wherein the user enters the generated login credentials to accessthe system to obtain the information service associated with the user.6. The system as claimed in claim 5, wherein the system stores consentof the user to store biometric features of the user for the class ofqueries for privileged information services, wherein upon receipt of theconsent of the user the system stores the biometric features of theuser, wherein the biometric features are stored based on the biometricscanners available in a user computing device associated with the user.7. The system as claimed in claim 1, wherein the ML engine is configuredto identify and authenticate said user through any or a combination ofvoice, password, OTP, facial feature, fingerprint, iris, DNA, skin, earlobe, nose and at least two or more biometric feature stored in thedatabase.
 8. The system as claimed in claim 1, wherein the ML engine isconfigured to identify whether the query is for generic informationservices or privileged information services, wherein the ML enginechecks whether the consent of user is available to access the privilegedinformation services.
 9. The system as claimed in claim 1, wherein theML engine is configured to apply and identify one or more authenticationmodules based on a predefined set of configuration parameters associatedwith the plurality of information services corresponding to the saidquery generated by the user.
 10. The system as claimed in claim 1,wherein the executable bot application is through any or a combinationof IVR, Native Dialler and OTT route.
 11. The system as claimed in claim1, wherein the ML engine is configured to change authentication modulebased on any or a combination of said query generated by the user andsaid user equipment and wherein the change of the authentication modulecorresponds to upgradation or down gradation of said authenticationmodule having said biometric features.
 12. The system as claimed inclaim 1, wherein the ML engine is configured to receive a query in theform of any or a combination of text, audio and video and wherein theresponse associated with the information service corresponding to thequery received by the ML engine is provided in the form of text, audioand video.
 13. The system as claimed in claim 1, wherein the ML engineis configured with language processing engines to receive said query inany language and provide said response corresponding to said query inany language.
 14. A method for authentication on the bot application,said method comprising: receiving a first set of data packets comprisinga video stream along with query from a user computing device associatedwith a user, said video stream along with said query pertaining tobiometric features of the user, and receive, from a database, aknowledgebase comprising a set of potential identity informationassociated with said biometric features of said user and a plurality ofinformation services associated with said user and said query;extracting, by a query extraction engine, a first set of features fromthe received first set of data packets, said first set of featuresassociated with a class of queries associated with the informationservice; extracting, by a biometric collecting engine, a second set offeatures from the received first set of data packets, said second set offeatures corresponding to biometric features of said user; mapping,through a machine learning (ML) engine, any or a combination ofextracted first and second set of features with said knowledgebase toidentify and authenticate said user and said query; generating, throughthe ML engine, a trained model configured to process said query of saididentified and authenticated user, and predict, from said plurality ofinformation services, an information service associated with saididentified and authenticated user query, and facilitate responsecorresponding to said information service to said identified andauthenticated user query based on the trained model; andauto-generating, using the ML engine, the response by the botapplication to said identified and authenticated user.
 15. The method asclaimed in claim 14, wherein the class of queries comprise a set ofqueries for information services, wherein the information services aregeneric information services and privileged information services. 16.The method as claimed in claim 14, wherein said query of the identifiedand authenticated user is received at client side of the executable botapplication in the form of a second set of data packets from said usercomputing device, and wherein said response that is mapped with theinformation service is transmitted in real-time in the form of a thirdset of data packets to said user computing device from server side ofthe bot application.
 17. The method as claimed in claim 14, wherein saidbot application is represented in the form of any or a combination of ananimated character, a personality character, or an actual representationof a human operator.
 18. The method as claimed in claim 14, wherein themethod is configured to obtain a registration data based on a requestfrom an unregistered user through respective user computing device,wherein login credentials are generated based on acknowledgement of therequest and verification of the registration data, wherein the userenters the generated login credentials to access the method to obtainthe information service associated with the user.
 19. The method asclaimed in claim 18, wherein the method stores consent of the user tostore biometric features of the user for the privileged set of queries,wherein upon receipt of the consent of the user the user stores thebiometric features of the user, wherein the biometric features arestored based on the biometric scanners available in a user computingdevice associated with the user.
 20. The method as claimed in claim 14,wherein the ML engine is configured to identify and authenticate saiduser through any or a combination of voice, password, OTP, facialfeature, fingerprint, iris, DNA, skin, ear lobe, nose and at least twoor more biometric feature stored in the database.
 21. The method asclaimed in claim 14, wherein the ML engine is configured to identifywhether the query is for generic information services or privilegedinformation services, wherein the ML engine checks whether the consentof user is available to access the privileged information services. 22.The method as claimed in claim 14, wherein the ML engine is configuredto apply and identify one or more authentication modules based on apredefined set of configuration parameters associated with the pluralityof information services corresponding to the said query generated by theuser.
 23. The method as claimed in claim 14, wherein the executable botapplication is through any or a combination of IVR, Native Dialler andOTT route.
 24. The method as claimed in claim 14, wherein the ML engineis configured to change authentication module based on any or acombination of said query generated by the user and said user equipmentand wherein the change of the authentication module corresponds toupgradation or down gradation of said authentication module having saidbiometric features.
 25. The method as claimed in claim 14, wherein theML engine is configured to receive a query in the form of any or acombination of text, audio and video and wherein the response associatedwith the information service corresponding to the query received by theML engine is provided in the form of text, audio and video.
 26. Themethod as claimed in claim 14, wherein the ML engine is configured withlanguage processing engines to receive said query in any language andprovide said response corresponding to said query in any language.